[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

Medical imaging toolkit for deep learning

Official Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" - MICCAI 2021

[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification

Automated lung segmentation in CT

Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.

A framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

Computational Pathology Toolbox developed by TIA Centre, University of Warwick.

Medical Image Analysis Lab (MIALab), University of Bern

A generalizable application framework for segmentation, regression, and classification using PyTorch

Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem. ⭐ Star to support our work!

A PyTorch Computer Vision (CV) module library for building n-D networks flexibly ~

This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"

Fully automatic brain tumour segmentation using Deep 3-D convolutional neural networks

Multi-Planar UNet for autonomous segmentation of 3D medical images

COVID deterioration prediction based on chest X-ray radiographs via MoCo-trained image representations

This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.

Robust Chest CT Image Segmentation of COVID-19 Lung Infection based on limited data